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Integrating Replenishment Decisions with Advance Demand Information

Author

Listed:
  • Guillermo Gallego

    (Department of Industrial Engineering and Operations Research, Columbia University, New York, New York 10027)

  • Özalp Özer

    (Department of Management Science and Engineering, Stanford University, Stanford, California 94305)

Abstract

There is a growing consensus that a portfolio of customers with different demand lead times can lead to higher, more regular revenues and better capacity utilization. Customers with positive demand lead times place orders in advance of their needs, resulting in advance demand information. This gives rise to the problem of finding effective inventory control policies under advance demand information. We show that state-dependent (s, S) and base-stock policies are optimal for stochastic inventory systems with and without fixed costs. The state of the system reflects our knowledge of advance demand information. We also determine conditions under which advance demand information has no operational value. A numerical study allows us to obtain additional insights and to evaluate strategies to induce advance demand information.

Suggested Citation

  • Guillermo Gallego & Özalp Özer, 2001. "Integrating Replenishment Decisions with Advance Demand Information," Management Science, INFORMS, vol. 47(10), pages 1344-1360, October.
    • Handle: RePEc:inm:ormnsc:v:47:y:2001:i:10:p:1344-1360
    DOI: 10.1287/mnsc.47.10.1344.10261
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    References listed on IDEAS

    as
    1. Jing-Sheng Song & Paul Zipkin, 1993. "Inventory Control in a Fluctuating Demand Environment," Operations Research, INFORMS, vol. 41(2), pages 351-370, April.
    2. Warren H. Hausman, 1969. "Sequential Decision Problems: A Model to Exploit Existing Forecasters," Management Science, INFORMS, vol. 16(2), pages 93-111, October.
    3. Arthur F. Veinott, Jr., 1965. "Optimal Policy for a Multi-Product, Dynamic, Nonstationary Inventory Problem," Management Science, INFORMS, vol. 12(3), pages 206-222, November.
    4. Suresh P. Sethi & Feng Cheng, 1997. "Optimality of ( s , S ) Policies in Inventory Models with Markovian Demand," Operations Research, INFORMS, vol. 45(6), pages 931-939, December.
    5. William S. Lovejoy, 1990. "Myopic Policies for Some Inventory Models with Uncertain Demand Distributions," Management Science, INFORMS, vol. 36(6), pages 724-738, June.
    6. Evan L. Porteus, 1971. "On the Optimality of Generalized (s, S) Policies," Management Science, INFORMS, vol. 17(7), pages 411-426, March.
    7. G. D. Johnson & H. E. Thompson, 1975. "Optimality of Myopic Inventory Policies for Certain Dependent Demand Processes," Management Science, INFORMS, vol. 21(11), pages 1303-1307, July.
    8. Donald L. Iglehart, 1963. "Optimality of (s, S) Policies in the Infinite Horizon Dynamic Inventory Problem," Management Science, INFORMS, vol. 9(2), pages 259-267, January.
    9. Jing-Sheng Song & Paul H. Zipkin, 1996. "Inventory Control with Information About Supply Conditions," Management Science, INFORMS, vol. 42(10), pages 1409-1419, October.
    10. Katy S. Azoury & Bruce L. Miller, 1984. "A Comparison of the Optimal Ordering Levels of Bayesian and Non-Bayesian Inventory Models," Management Science, INFORMS, vol. 30(8), pages 993-1003, August.
    11. Chen, F., 1999. "Market Segmentation, Advanced Demand Information and Supply Chain Performance," Papers 99-2, Columbia - Graduate School of Business.
    12. Matthew J. Sobel & Rachel Q. Zhang, 2001. "Inventory Policies for Systems with Stochastic and Deterministic Demand," Operations Research, INFORMS, vol. 49(1), pages 157-162, February.
    13. Rema Hariharan & Paul Zipkin, 1995. "Customer-Order Information, Leadtimes, and Inventories," Management Science, INFORMS, vol. 41(10), pages 1599-1607, October.
    14. George F. Brown, Jr. & Richmond M. Lloyd & Timothy M. Corcoran, 1971. "Inventory Models with Forecasting and Dependent Demand," Management Science, INFORMS, vol. 17(7), pages 498-499, March.
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